Название: Knowledge Recommendation Systems with Machine Intelligence AlgorithmsАвтор: Jarosław Protasiewicz
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 139
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB
Knowledge recommendation is an urgent and timely topic encountered in research and information services. There is a strongly compelling and urgent need: the modern economy badly requires highly skilled professionals, researchers, and innovators, which enables opportunities to gain competitive advantages and assist in managing financial resources and available goods, as well as carrying out fundamental and applied research more effectively.
From the structural perspective, knowledge recommendation comprises three main functional phases: assignment, recommendation, and finding people who are able to deliver knowledge or written artefacts, including articles and innovations. This structural point of view is fully reflected in the organisation of the book. In essence, it focuses on the recommendation of reviewers, experts, and innovation support. From the design point of view, one concentrates on requirement elicitation, architectural development, detailed design, validation, and verification.
The design, development, and implementation of the two representative IT systems discussed in the book supplemented with content-based recommendation algorithms illustrate how the paradigm and theory of knowledge recommendation work in practice. This also includes a way of the development and practical application of selected heuristics and Machine Learning/machine intelligence algorithms that aim to create individuals’ expertise profiles and to deliver ways of evaluating enterprise innovation.
The book contains an original material and is unique in many ways. The prudent and though-out selection and the exposure of the topics, depth of coverage of the subject matter, and original insights are the focal features of the book. New and promising directions and techniques of Machine Learning applied to knowledge recommendation are original. The critical literature review identifying the state-of-the-art of the area along with the main achievements in the area and existing limitations and challenges will appeal to the reader interested in getting acquainted with the intensive studies reported in the literature. Bringing theoretical and application-oriented facets of the area in a coherent way and forming a coherent view at the junction of theory and practice is a genuine asset of the book. The author himself has been actively involved in the design, deployment, and maintenance of the systems—this first-hand experience shared with the readers is another feature that speaks to the uniqueness of the book.
Скачать Knowledge Recommendation Systems with Machine Intelligence Algorithms